Building a bridge of bounding box regression between oriented and horizontal object detection in remote sensing images
Oriented object detection (OOD) aims to precisely detect the objects with arbitrary orientation
in remote sensing images (RSIs). Up to now, most of the bounding box regression (BBR) …
in remote sensing images (RSIs). Up to now, most of the bounding box regression (BBR) …
Instance-aware distillation for efficient object detection in remote sensing images
Practical applications ask for object detection models that achieve high performance at low
overhead. Knowledge distillation demonstrates favorable potential in this case by …
overhead. Knowledge distillation demonstrates favorable potential in this case by …
A unified transformer framework for group-based segmentation: Co-segmentation, co-saliency detection and video salient object detection
Humans tend to mine objects by learning from a group of images or several frames of video
since we live in a dynamic world. In the computer vision area, many researchers focus on co …
since we live in a dynamic world. In the computer vision area, many researchers focus on co …
Mining high-quality pseudoinstance soft labels for weakly supervised object detection in remote sensing images
Weakly supervised object detection in remote sensing images (RSI) is still a challenge
because of the lack of instance-level labels, and many existing methods have two problems …
because of the lack of instance-level labels, and many existing methods have two problems …
[HTML][HTML] Semantic segmentation guided pseudo label mining and instance re-detection for weakly supervised object detection in remote sensing images
Weakly supervised object detection (WSOD) in remote sensing images (RSIs) has good
practical value because it only requires the image-level annotations. The existing methods …
practical value because it only requires the image-level annotations. The existing methods …
Dynamic low-rank and sparse priors constrained deep autoencoders for hyperspectral anomaly detection
Linear-based low-rank and sparse models (LRSM) and nonlinear-based deep autoencoder
(DAE) models have been proven to be effective for the task of anomaly detection (AD) in …
(DAE) models have been proven to be effective for the task of anomaly detection (AD) in …
ECAE: Edge-aware class activation enhancement for semisupervised remote sensing image semantic segmentation
W Miao, Z Xu, J Geng, W Jiang - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Remote sensing image semantic segmentation (RSISS) remains challenging due to the
scarcity of labeled data. Semisupervised learning can leverage pseudolabels to enhance …
scarcity of labeled data. Semisupervised learning can leverage pseudolabels to enhance …
Multiform ensemble self-supervised learning for few-shot remote sensing scene classification
Self-supervised learning is an effective way to solve model collapse for few-shot remote
sensing scene classification (FSRSSC). However, most self-supervised contrastive learning …
sensing scene classification (FSRSSC). However, most self-supervised contrastive learning …
Hyperspectral anomaly detection via sparse representation and collaborative representation
S Lin, M Zhang, X Cheng, K Zhou… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
Sparse representation (SR)-based approaches and collaborative representation (CR)-
based methods are proved to be effective to detect the anomalies in a hyperspectral image …
based methods are proved to be effective to detect the anomalies in a hyperspectral image …
Smooth giou loss for oriented object detection in remote sensing images
X Qian, N Zhang, W Wang - Remote Sensing, 2023 - mdpi.com
Oriented object detection (OOD) can more accurately locate objects with an arbitrary
direction in remote sensing images (RSIs) compared to horizontal object detection. The most …
direction in remote sensing images (RSIs) compared to horizontal object detection. The most …